Top 10 Agentic Ai Examples And Use Cases Pellera Com

Bonisiwe Shabane

-Dec 13, 2025, 12:45 AM

top 10 agentic ai examples and use cases pellera com

By: Brian Sabzjadid In the ever-evolving landscape of artificial intelligence, a new paradigm is emerging that promises to redefine how we get work done: “Agentic AI”. Picture this: It’s the near future, and you’re at your favorite café, sipping on a cappuccino,... By: Jonathan D. Gough, PhD The integration of artificial intelligence (AI) into business operations is not just a technological leap—it’s a strategic necessity. For organizations beginning their AI journeys, one essential question arises: How should they approach... By: Greg Berard Building Momentum for What’s Next Change can feel intimidating, but at Pellera, we see it as an incredible opportunity to rethink, reimagine, and reinvent.

That’s the mindset driving us forward every day. With the launch of Season 2 of our podcast,... By: Brian Sabzjadid In the ever-evolving landscape of artificial intelligence, a new paradigm is emerging that... By: Jonathan D. Gough, PhD The integration of artificial intelligence (AI) into business operations is not just a... Home / Blogs / 10 Agentic AI Examples (Use Cases) for Enterprises & How To Build Them

Learn how Astera Data Stack can simplify and streamline your enterprise’s data management. AI is no longer just a tool. It is now handling complex tasks with minimal human intervention and oversight. This transformative shift has given rise to agentic AI, where AI-powered systems make decisions, adapt to new information, and automate workflows across departments. From answering customer inquiries to managing financial data, these AI-driven agents are reshaping how businesses operate. Companies are using them to improve efficiency, enhance decision-making, and reduce manual effort across customer service, sales, marketing, HR, IT, and finance.

To show (not tell) you the potential of agentic AI, we’re covering the top ten agentic AI examples that help enterprises automate tasks, streamline operations, and improve productivity. Agentic AI automation-powered by autonomous AI agents that learn, adapt, and make independent decisions-is revolutionizing how businesses operate across every sector. Unlike traditional task automation, agentic systems offer dynamic, context-aware solutions that can dramatically enhance efficiency, reduce costs, and improve customer experiences. From manufacturing floors to retail stores, legal offices to energy grids, organizations are discovering that agentic AI can handle complex, multi-step processes that previously required extensive human intervention. According to recent industry analysis, companies implementing agentic automation are seeing operational cost reductions of 20-40% while simultaneously improving service quality and response times. Below, we explore ten transformative use cases across diverse industries to illustrate how agentic automation is driving unprecedented change.

Healthcare providers struggle with fragmented patient information scattered across multiple systems and departments. This fragmentation leads to delayed treatments, missed follow-ups, and dangerous care gaps that can compromise patient outcomes. Agentic AI Advantage: Autonomous agents monitor patient data across systems, coordinate appointments, track treatment progress, and alert providers to critical changes in real-time. From cybersecurity to supply chain management, agentic AI can help businesses automate complex, multistep tasks in real time. The term agentic AI, or AI agents, refers to AI systems capable of independent decision-making and autonomous behavior. These systems can reason, plan and perform actions, adapting in real time to achieve specific goals.

Unlike traditional automation tools that follow predetermined pathways, agentic AI doesn't rely on a fixed set of instructions. Instead, it uses learned patterns and relationships to determine the best approach to achieving an objective. To do this, agentic AI breaks down a larger main objective into smaller subtasks, said Thadeous Goodwyn, director of generative AI at Booz Allen Hamilton. These subtasks are then delegated to more specialized AI models, often using more traditional, narrow AI models for specific actions. The decisions and actions of these component AI systems ultimately enable the AI agent to achieve its primary objective. And this capability is quickly maturing, according to Goodwyn.

Agentic artificial intelligence (AI) is transitioning from pilot projects to concrete applications for business-critical processes. You can find agentic AI examples in all industries, and use cases are expanding. Companies are adopting these systems despite implementation challenges and inherent risks. According to a 2025 Gravitee survey, approximately 72% of medium-sized companies and large enterprises currently use agentic AI, and an additional 21% plan to adopt it within the next two years. The global market is predicted to grow from $5.2 billion in 2024 to $196.6 billion in 2034. Should you embrace this technology now or wait until it matures?

Our article breaks down current real-life agentic AI use cases and shows how the technology can benefit companies. Agentic AI is a semi-autonomous, self-learning, and deterministic system capable of handling complex tasks. It can learn from past interactions, make real-time decisions, plan execution, adjust behavior based on real-time data, and coordinate other tools and APIs. Here’s how it works: first, you assign an objective and establish constraints (rules). Agentic AI then interprets your goals, breaks them into subtasks, and plans how to accomplish all the tasks. The system uses third-party apps and databases, adjusts execution of its plan based on output, and studies the results to learn from mistakes.

The enterprise software landscape is already undergoing a seismic transformation with the widespread adoption of Agentic AI. Organizations worldwide that were earlier grappling with mounting operational complexity, talent shortages, and relentless pressure, now find peace with Agentic AI for their accelerated digital transformation. While traditional AI offered pattern recognition and predictive analytics, it required constant human intervention and lacked true decision-making autonomy. Enter agentic A, the much popular autonomous systems that don't just analyze data but independently plan, execute, and adapt to achieve specific business goals. According to Gartner’s recent industry projections, 40% of enterprise applications will integrate task-specific AI agents by the end of 2026, representing a dramatic leap from less than 5% in 2025. This blog examines the top 10 agentic AI use cases that are transforming businesses in 2026, exploring how autonomous systems are reshaping operations across customer experience, engineering, security, finance, and other areas.

Agentic AI represents autonomous artificial intelligence systems designed to accomplish specific goals with minimal human supervision independently. Unlike traditional AI models that follow predefined rules or respond to explicit prompts, agentic AI possesses agency—the ability to perceive its environment, make informed decisions in context, and take action toward achieving its objectives.Traditional... A customer service chatbot, for instance, retrieves answers from a knowledge base based on keyword matching but cannot resolve issues requiring multi-step workflows. Traditional AI excels at specific tasks like image recognition, sentiment analysis, or data classification but lacks the autonomy to adapt strategies when encountering unexpected scenarios. Agentic AI fundamentally differs in three core capabilities. The architectural distinction is equally significant.

Agentic AI systems typically orchestrate multiple specialized large language models that communicate through sophisticated prompts, access external tools via APIs, and maintain persistent memory across interactions. One model might function as a task manager, decomposing complex problems and distributing work to specialized sub-agents that complete assignments and return outputs for evaluation. With Agentic AI marking a fundamental shift in enterprise technology strategy, organizations are transitioning from experimentation to scaled deployment. Interestingly, this acceleration reflects a maturation beyond pilot projects toward production-grade autonomous systems embedded in core business processes.Now, Agentic AI is gaining recognition as a strategic enabler of transformation particularly in service organizations where... The focus has shifted from data cleanup and governance toward building agentic workflows, integrating AI into core platforms, and scaling safely across the enterprise.Industry adoption of agentic AI use cases is increasing worldwide, with... Think of a virtual digital assistant that doesn’t just reply to a prompt but acts, triages leads, schedules follow-ups, conducts QA on conversations, and even takes steps across systems to get things done.

That’s agentic AI: a class of AI that operates with autonomy, context, and the ability to perform multi-step tasks on your behalf. In this post we’ll walk through what agentic AI actually means, why small and large businesses should care, and the top use cases you can practically deploy today. Most conversational AI today is reactive, answering questions, following scripts, or surfacing suggestions. That still leaves humans doing the heavy lifting: doing follow-ups, making decisions, or updating downstream systems. Agentic AI is built to act. It takes multi-step decisions, orchestrates workflows across channels and systems, remembers context across interactions, and executes tasks (qualify a lead, reschedule an appointment, escalate a claim) with minimal human supervision.

Think of it as a team member that can reason about the next steps and actually perform them. Below are ten practical, revenue- and ops-focused use cases where agentic AI moves the needle, across BFSI, customer service, e-commerce, healthcare, edtech, and automotive. Problem: Sales reps lose time on low-fit leads or slow follow-ups. Autonomous generative AI agents execute complex tasks with little or no human supervision. Agentic AI differs from chatbots and co-pilots. Unlike traditional AI, particularly generative AI, which often requires human intervention in complex workflows, agentic AI aims to autonomously navigate and optimize processes thanks to its decision-making capabilities and goal-directed behavior.

AI agents serve as: AI code editors like Cursor AI Editor, Windsurf Editor, and Replit aims to build and deploy apps (e.g. To-Do list app) by: A developer used OpenAI’s Operator and Replit’s AI Agent to build an entire app in 90 minutes. Two agents autonomously exchanged credentials, and ran tests. Cursor’s agent mode Composer aims to generate a complete Tic Tac Toe game from a single prompt:“Generate an HTML, CSS, and JavaScript Tic Tac Toe game for 2 players.”

Top 10 Agentic Ai Examples And Use Cases Pellera Com

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